An adversarial example is an item or input which is slightly modified but in such a way that it does not look different from the original example but the machine learning model misclassifies it. The modifications to the original examples are done in such a way that the user is unable to tell the difference but the model produces incorrect output.
Understand the basics of Adversarial Examples and why it makes Machine Learning models fail ðŸ¤®In this article, we see why adversarial examples are not bugs but are features of Machine Learning models.
Key points:
 Adversarial examples arise due to the presence of nonrobust features.
 The nonrobust features are captured within a theoretical framework and their widespread existence is established in standard datasets.
 Phenomena observed in practice is due to a misalignment between the human definition of robustness and the inherent geometry of the data.
Hypothesis:
Adversarial vulnerability is a direct result of our models' sensitivity to wellgeneralising features in the data
Implications
 Explains adversarial transferability, which means that adversarial perturbations computed for one model can transfer to other independentlytrained models
 Attributes adversarial vulnerability to a humancentric phenomenon
Previous hypotheses:
Adversarial examples are aberrations that arise because of
 The high dimensional nature of the input space
 Statistical flunctuations in the training datas
Impact: Adversarial robustness is treated as a goal to be disentangled and pursued independently from maximimising accuracy, by (1) improving standard regularisation methods and (2) pre/postprocessing of network inputs/outputs
Definitions:
Adversarial examples: Natural inputs that induce erroneous predictions in classifiers
Features: derived from patterns in the data distribution
Nonrobust features: Highly predictive features that are brittle and thus incomprehensible to humans
Why do adversarial perturbations arise?
As classifiers are trained to only maximise distributional accuracy, they use any available signal to do so regardless of whether it is comprehensible to humans, as they are indifferent to any other predictive feature. The "nonrobust" features can lead to adversarial perturbations.
Implementation 1: Finding robust and nonrobust features
Robust features can be disentangled from nonrobust features in standard image classification sets.
 A robustified version for robust classification can be constructed for any training set, where standard training on a training set yields good robust accuracy on the original, unmodified test set, implying that adversarial vulnerability is a property of the dataset.
 A nonrobust version for standard classification can be constructed for a training dataset with input similar to the original, but seemingly "incorrectly labeled".Inputs are associated to their labels through small adversarial perturbations and only use nonrobust features  perturbations can arise from flipping features in the data useful for classifying correct inputs
Robust features model
Setup:
 Binary classification is used, inputlabel pairs (x,y) that belong to (X x {+/ 1}) are sampled from a data distribution D.
 The goal is to learn a classifier C: X towards {+/ 1}, which will produce a label y corresponding to a given input.
Constraints

A feature maps from the input space X to the real numbers

A set of all features is F = {f : X > R}, the features in F are scaled for mean = 0, var = 1 for the following definitions to be scaleinvariant

Useful, robust and nonrobust features:
 puseful: a feature is puseful (p>0) for a given distribution D if it is correlated with the true label in expectation and p_(D)(f) as the largest p for which feature f is puseful under distribution D, i.e.
 yrobustly useful features: f is a robust feature if under adversarial perrtubation, f remains yuseful. It is also a robustly useful feature f(p_(D)(f)>0) for y > 0. I.e.
 Useful, nonrobust features: fetures that re puseful for p bounded away from 0, but not a yrobust feature for any y>=0  they help with classification in standard settings but hinder accuracy in adversarial settings as the correlation with the label can be flipped
 puseful: a feature is puseful (p>0) for a given distribution D if it is correlated with the true label in expectation and p_(D)(f) as the largest p for which feature f is puseful under distribution D, i.e.

The classifier used in classfication tasks to predict the label y is shown as follows.
Training
The classifier was trained by minimising a loss function with empirical risk optimisation  which decreased with the correlation between weighted combination of the features of the label
Standard training
 No distinction exists between robust and nonrobust features when minimising classification loss  only the pusefulness matters
 The classifier will use any puseful feature in F to decrease the loss of the classifier
Robust training
 In the presence of an adversary, any useful but nonrobust features can be made anticorrelated with the true label  leads to adversarial vulnerability, so the effect of the adversary must be explicitly accounted for
 The above implies that ERM is no longer sufficient to train robust classifiers, so an adversarial loss function should be used
 In other words, the classifier can no longer learn a useful but nonrobust combination of features
Finding robust and nonrobust features model
 This is based on the framework that both robust and nonrobust features exist that constitute useful signals for standard classifications
 Two datasets will be constructed as follows
Dataset 1 : Robustified dataset
 Samples that primarily contain robust features
 Robust classifiers can be trained using standard training
 Implication 1: Robustness can arise by removing certain features from the dataset, i.e. the new dataset contains less information about the original training set
 Implication 2: Adversarial vulnerability is caused by nonrobust features, not inherently tied to the standard training framework
Dataset 2 : Inputlabel association based purely on nonrobust features
 Dataset appears completely mislabeled to humans
 Can train a classifier with good performancce on the standard test set
 Implication 1: Natural models use nonrobust features to make predictions even with the presence of robust features
 Implication 2: Nonrobust features alone are sufficient for nontrivial generalisations performance on natural images  nonrobust features are valuable features, not artifacts of finitesample overfitting
Sidebyside experiment setup
On the left are random examples from the variants of the CIFAR10 [Kri09] training set: the original training set; the robust training set D_R, restricted to features used by a robust model; and the nonrobust training set D_NR, restricted to features relevant to a standard model (labels appear incorrect to humans).
On the right are standard and robust accuracy on the CIFAR10 test set (D) for models training with: (i) standard training (on D); (ii) standard training on D_NR; (iii) adversarial training (on D); and (iv) standard training on D_R. Models training on D_R and D_NR reflect the original models used to create them: notably, standard training on D_R yields nontrivial robust accuracy.
Disentangling robust and nonrobust features
In order to manipulate the features of networks which may be complex and highdimensional, a robust model can be leveraged and the dataset modified to contain only relevant features

A robust model C can be used to construct a distribution D_R to satisfy the following conditions:

A training set D_R can be constructed with one to one mapping of x to x_r from the original training set for D. x is the original input and g is the mapping from x to the representation layer. All features in F_C can be enforced to have similar values for x and x_r:
Results: A classifier is trained with standard training then tested on the original data set D. The classifier learned using the new dataset attained good accuracy in standard and adversarial settings.
Nonrobust features suffice for standard classification
 Hypothesis: A model trained solely on nonrobust features can perform well on the standard test set
 Implementation: Dataset where only features useful for classification are nonrobust, formally:
 A Dataset D_rand should be constructed as follows
 And a Dataset D_det as follows
The below table shows the test accuracy of classifiers trained on the D, D_rand and D_det training sets
Nonrobust features are useful for classification in the standard setting, as standard training on the above datasets generalise to the original data set
The above figure demonstrates the transfer rate of adversarial examples from a ResNet50 to different architectures alongside test set performance of these architecture when trainined on the dataset generated in Section 3.2. Architectures more suspectible to transfer attackes also performed better on the standard test set supporting the hypothesis that adversarial transferability arises from utilising similar nonrobust features.
Transferability can arise from nonrobust features
Adversarial examples transfer across models with different architectures and independently sampled training sets
Setup: Train five different architectures for a standard ResNet50
Hypothesis: Architectures which learn better from this training set compared to the standard test set are more likely to learn similar nonrobust features to the original classifier
Implementation 2: Studying (Non)Robust Features
A concrete classification task for the purpose of studying adversarial examples and nonrobust features, based on Tsipras et al as it contains a dichotomy between robust and nonrobust features
Summary:
 Separating Gaussian distributions
 Can precisely quantify adversarial vulnerability as the difference between the intrinsic data geometry and the adversary's perturbation set
 Classifier from using robust training corresponding to a combination of the intrinsic data geometry and the adversary's perturbation set
 Gradients of standard models can be more misaligned with the interclass direction
Setup:
Problem  maximum likelihood classification between two Gaussian distributions, i.e.
given samples (x, y) from D
the goal is to learn parameters Theta = (mu, Sigma)
with l(x; mu, Sigma) representing the Gaussian negative loglikelihood function
Classification under this model can be accomplished via likelihood test: for unlabeled sample x, we predict y as
Robust analogue of this problem arises from l(x; y.mu, Sigma) with the NLL under adversarial perturbation
Results
 Vulnerability from metric misalignment  adversarial vulnerability aroses as a misalignment of of two metrics
 Robust Learning
Empical demonstration:
An empirical demonstration of the effect illustrated by Therorem 2 is shown above, as the adversarial pertubation budget e is increased, the learned mean mu remains constant, but the learned covariance blends with the identity matrix, effectively adding more and more uncertainty onto the nonrobust feature.  Gradient interpretability
Implications:
 Adversarial examples are a fundamentally human phenomenon
 Classifiers exploit highly predictive features that happen to be nonrobust under a humanselected notion of similarity (because these features exist in realworld datasets)
 We cannot expect to have model explanations that are both humanmeaningful and faithful to the models themselves as models rely on nonrobust features
 For robust, interpretable models, it will be necessary to explicitly encode human priors into the training process
Robustness and accuracy:
 Robustness can be at odds with accuracy  training prevents us from learning the most accurate classifier
 Nonrobust features manifest themselves in the same way, yet a classifier with perfect robustness and accuracy is still attainable
 Any standard loss function will learn an accurate yet nonrobust classifier
 Classifier learns a perfectly accurate and perfectly robust decision boundary when robust training is employed
Further readings:
 Adversarial Machine Learning by Apoorva Kandpal
 Adversarial examples in the Physical world by Apoorva Kandpal
 Explaining and Harnessing Adversarial examples by Ian Goodfellow by Murugesh Manthiramoorthi
 Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images by Murugesh Manthiramoorthi